Offloading in HetNet: A Coordination of Interference Mitigation, User Association, and Resource Allocation

The use of heterogeneous small cell-based networks to offload the traffic of existing cellular systems has recently attracted significant attention. One main challenge is solving the joint problems of interference mitigation, user association, and resource allocation. These problems are formulated as an optimization which is then analyzed using two different approaches: Markov approximation and log-linear learning. However, finding the optimal solutions of both approaches requires complete information of the whole network which is not scalable with the network size. Thus, an approach based on a Markov approximation with a novel Markov chain design and transition probabilities is proposed. This approach enables the Markov chain to converge to the bounded near optimal distribution without complete information. In the game-theoretic approach, the payoff-based log-linear learning is used, and it converges in probability to a mixed-strategy ε-Nash equilibrium. Based on the principles of these two approaches, a highly randomized self-organizing algorithm is proposed to reduce the gap between optimal and converged distributions. Simulation results show that all of the proposed algorithms effectively offload more than 90 percent of the traffic from the macrocell base station to small cell base stations. Moreover, the results also show that the algorithms converge quickly irrespective of the number of possible configurations.